fixes

NAMESPACE

@@ -387,9 +387,9 @@ export(set_AMR_locale)
 export(set_ab_names)
 export(set_mo_source)
 export(sir_confidence_interval)
+export(sir_df)
 export(sir_interpretation_history)
 export(sir_predict)
-export(sir_sf)
 export(skewness)
 export(streptogramins)
 export(susceptibility)

R/bug_drug_combinations.R

@@ -37,7 +37,7 @@
 #' @param FUN the function to call on the `mo` column to transform the microorganism codes, defaults to [mo_shortname()]
 #' @param translate_ab a [character] of length 1 containing column names of the [antibiotics] data set
 #' @param ... arguments passed on to `FUN`
-#' @inheritParams sir_sf
+#' @inheritParams sir_df
 #' @inheritParams base::formatC
 #' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_SI = TRUE` (default) to test R vs. S+I and `combine_SI = FALSE` to test R+I vs. S.
 #' @export

R/count.R

@@ -41,7 +41,7 @@
 #'
 #' The function [n_sir()] is an alias of [count_all()]. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to `n_distinct()`. Their function is equal to `count_susceptible(...) + count_resistant(...)`.
 #'
-#' The function [count_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and counts the number of S's, I's and R's. It also supports grouped variables. The function [sir_sf()] works exactly like [count_df()], but adds the percentage of S, I and R.
+#' The function [count_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and counts the number of S's, I's and R's. It also supports grouped variables. The function [sir_df()] works exactly like [count_df()], but adds the percentage of S, I and R.
 #' @inheritSection proportion Combination Therapy
 #' @seealso [`proportion_*`][proportion] to calculate microbial resistance and susceptibility.
 #' @return An [integer]

R/ggplot_sir.R

@@ -53,7 +53,7 @@
 #' @details At default, the names of antibiotics will be shown on the plots using [ab_name()]. This can be set with the `translate_ab` argument. See [count_df()].
 #'
 #' ### The Functions
-#' [geom_sir()] will take any variable from the data that has an [`sir`] class (created with [as.sir()]) using [sir_sf()] and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
+#' [geom_sir()] will take any variable from the data that has an [`sir`] class (created with [as.sir()]) using [sir_df()] and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
 #'
 #' [facet_sir()] creates 2d plots (at default based on S/I/R) using [ggplot2::facet_wrap()].
 #'
@@ -340,7 +340,7 @@ geom_sir <- function(position = NULL,
 
   ggplot2::geom_col(
     data = function(x) {
-      sir_sf(
+      sir_df(
         data = x,
         translate_ab = translate_ab,
         language = language,
@@ -521,7 +521,7 @@ labels_sir_count <- function(position = NULL,
     colour = datalabels.colour,
     lineheight = 0.75,
     data = function(x) {
-      transformed <- sir_sf(
+      transformed <- sir_df(
         data = x,
         translate_ab = translate_ab,
         combine_SI = combine_SI,

R/proportion.R

@@ -53,7 +53,7 @@
 #'
 #' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` argument).*
 #'
-#' The function [proportion_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and calculates the proportions S, I, and R. It also supports grouped variables. The function [sir_sf()] works exactly like [proportion_df()], but adds the number of isolates.
+#' The function [proportion_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and calculates the proportions S, I, and R. It also supports grouped variables. The function [sir_df()] works exactly like [proportion_df()], but adds the number of isolates.
 #' @section Combination Therapy:
 #' When using more than one variable for `...` (= combination therapy), use `only_all_tested` to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
 #'
@@ -206,11 +206,11 @@
 #'     proportion_df(translate = FALSE)
 #'
 #'   # It also supports grouping variables
-#'   # (use sir_sf to also include the count)
+#'   # (use sir_df to also include the count)
 #'   example_isolates %>%
 #'     select(ward, AMX, CIP) %>%
 #'     group_by(ward) %>%
-#'     sir_sf(translate = FALSE)
+#'     sir_df(translate = FALSE)
 #' }
 #' }
 resistance <- function(...,

R/sir_calc.R

@@ -372,5 +372,5 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
 
   rownames(out) <- NULL
   out <- as_original_data_class(out, class(data.bak)) # will remove tibble groups
-  structure(out, class = c("sir_sf", class(out)))
+  structure(out, class = c("sir_df", "rsi_df", class(out)))
 }

R/sir_df.R

@@ -29,7 +29,7 @@
 
 #' @rdname proportion
 #' @export
-sir_sf <- function(data,
+sir_df <- function(data,
                    translate_ab = "name",
                    language = get_AMR_locale(),
                    minimum = 30,

R/zzz.R

@@ -98,14 +98,14 @@ if (utf8_supported && !is_latex) {
   s3_register("pillar::pillar_shaft", "av")
   s3_register("pillar::pillar_shaft", "mo")
   s3_register("pillar::pillar_shaft", "sir")
-  s3_register("pillar::pillar_shaft", "rsi") # TODO deprecate in a later version
+  s3_register("pillar::pillar_shaft", "rsi") # remove in a later version
   s3_register("pillar::pillar_shaft", "mic")
   s3_register("pillar::pillar_shaft", "disk")
   s3_register("pillar::type_sum", "ab")
   s3_register("pillar::type_sum", "av")
   s3_register("pillar::type_sum", "mo")
   s3_register("pillar::type_sum", "sir")
-  s3_register("pillar::type_sum", "rsi")
+  s3_register("pillar::type_sum", "rsi") # remove in a later version
   s3_register("pillar::type_sum", "mic")
   s3_register("pillar::type_sum", "disk")
   # Support for frequency tables from the cleaner package

inst/tinytest/test-count.R

@@ -103,10 +103,10 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0")) {
     )
   )
 
-  # grouping in sir_calc_df() (= backbone of sir_sf())
+  # grouping in sir_calc_df() (= backbone of sir_df())
   expect_true("ward" %in% (example_isolates %>%
     group_by(ward) %>%
     select(ward, AMX, CIP, gender) %>%
-    sir_sf() %>%
+    sir_df() %>%
     colnames()))
 }

man/count.Rd

@@ -67,7 +67,7 @@ The function \code{\link[=count_resistant]{count_resistant()}} is equal to the f
 
 The function \code{\link[=n_sir]{n_sir()}} is an alias of \code{\link[=count_all]{count_all()}}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{n_distinct()}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
 
-The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and counts the number of S's, I's and R's. It also supports grouped variables. The function \code{\link[=sir_sf]{sir_sf()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
+The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and counts the number of S's, I's and R's. It also supports grouped variables. The function \code{\link[=sir_df]{sir_df()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
 }
 \section{Interpretation of SIR}{
 

man/ggplot_sir.Rd

@@ -120,7 +120,7 @@ Use these functions to create bar plots for AMR data analysis. All functions rel
 At default, the names of antibiotics will be shown on the plots using \code{\link[=ab_name]{ab_name()}}. This can be set with the \code{translate_ab} argument. See \code{\link[=count_df]{count_df()}}.
 \subsection{The Functions}{
 
-\code{\link[=geom_sir]{geom_sir()}} will take any variable from the data that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) using \code{\link[=sir_sf]{sir_sf()}} and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
+\code{\link[=geom_sir]{geom_sir()}} will take any variable from the data that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) using \code{\link[=sir_df]{sir_df()}} and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
 
 \code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
 

man/proportion.Rd

@@ -12,7 +12,7 @@
 \alias{proportion_SI}
 \alias{proportion_S}
 \alias{proportion_df}
-\alias{sir_sf}
+\alias{sir_df}
 \title{Calculate Microbial Resistance}
 \source{
 \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
@@ -52,7 +52,7 @@ proportion_df(
   confidence_level = 0.95
 )
 
-sir_sf(
+sir_df(
   data,
   translate_ab = "name",
   language = get_AMR_locale(),
@@ -102,7 +102,7 @@ Use \code{\link[=sir_confidence_interval]{sir_confidence_interval()}} to calcula
 
 These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[=count]{count()}} functions to count isolates. The function \code{\link[=susceptibility]{susceptibility()}} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can influence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} argument).}
 
-The function \code{\link[=proportion_df]{proportion_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and calculates the proportions S, I, and R. It also supports grouped variables. The function \code{\link[=sir_sf]{sir_sf()}} works exactly like \code{\link[=proportion_df]{proportion_df()}}, but adds the number of isolates.
+The function \code{\link[=proportion_df]{proportion_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and calculates the proportions S, I, and R. It also supports grouped variables. The function \code{\link[=sir_df]{sir_df()}} works exactly like \code{\link[=proportion_df]{proportion_df()}}, but adds the number of isolates.
 }
 \section{Combination Therapy}{
 
@@ -270,11 +270,11 @@ if (require("dplyr")) {
     proportion_df(translate = FALSE)
 
   # It also supports grouping variables
-  # (use sir_sf to also include the count)
+  # (use sir_df to also include the count)
   example_isolates \%>\%
     select(ward, AMX, CIP) \%>\%
     group_by(ward) \%>\%
-    sir_sf(translate = FALSE)
+    sir_df(translate = FALSE)
 }
 }
 }